OPTIMAL CONTROL OF BIOLOGICAL INVASIONS IN LAKE NETWORKS
Bibliographic record
Abstract
ABSTRACT. A metapopulation model for alien species invasion of a lake network is coupled with an economic model of prevention. The model restates a stochastic problem in deterministic terms. It provides a macroscopic description of the lake network with prevention methods controlling both the outflow of invaders at infected lakes and the inflow of invaders at uninfected lakes. Results indicate that optimal control implements no more than one of these methods at any moment in time. Typical optimal control measures change over time as the lake ecosystem becomes successively more invaded. Early control of outflow from infected lakes is replaced by later control of inflow to remaining uninfected lakes. Closed‐loop control trajectories are analytically characterized in the phase‐plane for a limiting case, while in general a simple and stable numerical algorithm is developed for solving the optimal control problem. This research has been supported by a grant from the National Science Foundation (DEB 02–13698). The second author acknowledges support from Canada Research Chair, an NSERC Collaborative Research Opportunity grant, and an NSERC Discovery grant.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".